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Phylogenetic clustering of wingbeat frequency and flight-associated morphometrics across orders

by Tercel, M.P.T.G., Veronesi, F. and Pope, T.W.

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DOI: https://doi.org/10.1111/phen.12240

Tercel, M.P.T.G., Veronesi, F. and Pope, T.W. 2018. Phylogenetic clustering of wingbeat frequency and flight‐associated morphometrics across insect orders. Physiological Entomology.

24 March 2018 1 Title

2 Phylogenetic clustering of wingbeat frequency and flight-associated morphometrics across

3 insect orders

4

5 Running title

6 Flight strategies between insect orders

7

8 Authors

9 Maximillian P. T. G. Tercel*, Fabio Veronesi and Tom W. Pope

10

11 Affiliation

12 Department of Crop and Environment Science, Harper Adams University, Newport,

13 Shropshire, TF10 8NB, UK.

14

15 *Author for correspondence ([email protected])

16

17

18 KEY WORDS: Comparative morphology, wing loading, body mass, flight strategy, insect

19 evolution, high-speed filming.

20

21

22 23 Summary statement

24 Insect flight strategy varies between orders but is generally well conserved within orders, this

25 has important evolutionary and ecological implications at high taxonomic levels.

26

27 ABSTRACT

28 Wingbeat frequency in is an important variable in aerodynamic and energetic

29 analyses of insect flight and has been studied previously on a family- or -level basis.

30 Meta-analyses of these studies have found order-level patterns that suggests flight strategy

31 is moderately well conserved phylogenetically. Studies incorporated into these meta-

32 analyses, however, use variable methodologies across different temperatures that may

33 confound results and phylogenetic patterns. Here, a high-speed camera was used to

34 measure wingbeat frequency in a wide variety of species (n = 102) in controlled conditions to

35 determine the validity of previous meta-analyses that show phylogenetic clustering of flight

36 strategy and to identify new evolutionary patterns between wingbeat frequency, body mass,

37 wing area, wing length, and wing loading at the order level. All flight-associated

38 morphometrics significantly affected wingbeat frequency. Linear models show that wing area

39 explained the most amount of variation in wingbeat frequency (R2 = 0.59, p = <0.001), whilst

40 body mass explained the least (R2 = 0.09, p = <0.01). A multiple regression model

41 incorporating both body mass and wing area was the best overall predictor of wingbeat

42 frequency (R2 = 0.84, p = <0.001). Order-level phylogenetic patterns across relationships

43 were consistent with previous studies. Thus, the present study provides experimental

44 validation of previous meta-analyses and provides new insights into phylogenetically

45 conserved flight strategies across insect orders.

46

47 48 INTRODUCTION

49 Wingbeat frequency in insects varies with body mass and wing area within and between

50 species (Byrne et al., 1988; Dudley, 2000), from 5.5 Hz in the helicopter damselfly

51 Megaloprepus caerulatus (Rüppell and Fincke, 1989) to over 1000 Hz in a ceratopogonid

52 Forcipomyia sp. midge (Sotavalta, 1953). How frequently an insect beats its wings is an

53 important variable when considering the biomechanics and physiology of insect flight

54 (Ellington, 1984a-f; Dudley, 2000; Alexander, 2002; Vogel, 2013). For any given body mass,

55 variables such as wing length, wing area, wing loading (body mass/wing area), wingbeat

56 frequency and stroke amplitude can differ substantially and affect the energetics and

57 biomechanics of insect flight, which is usually linked to evolutionary history (Byrne et al., 1988).

58 Stroke amplitude, the angle between the points of wing reversal, has been shown to vary

59 between taxa, from 66o in syrphids (Ellington, 1984c) to 180o in (Atkins, 1960) and

60 (Wilkins, 1991) and may vary significantly during a single flight as shown in dragonflies

61 (Alexander, 1986), orchid (Dudley, 1995; Dillon and Dudley, 2004), and fruit

62 (Lehmann and Dickinson, 1998; Fry et al., 2003). Though undeniably important to

63 understanding insect flight strategy and aerodynamics, stroke amplitude was not measured in

64 the current study. This is because although both wingbeat frequency and stroke amplitude

65 change during a single flight, wingbeat frequency is kept relatively constant because of the

66 high energetic cost of deviating from the resonant frequency of the flight apparatus (Dudley,

67 2000). Conversely, stroke amplitude may be altered extremely rapidly to change direction (Fry

68 et al., 2003) or flight mode e.g. from hovering to forward flight (Dillon and Dudley, 2004).

69 Because of this variability, stroke amplitude is likely to be a slightly less reliable indicator of

70 flight strategy than wingbeat frequency.

71 The variables that influence the energetic and biomechanical aspects of flight could be used

72 to broadly characterize flight strategies between different orders of insects. Typically, higher

73 wingbeat frequencies are associated with insects of smaller size, to overcome the increasingly

74 viscous forces of the air present at small spatial scales, represented by low Reynolds numbers 75 Re in the order of 10-100 in the smallest insects (Ellington, 1999; Wang, 2005), and to better

76 control their direction in a windswept world (Vogel, 2013). Furthermore, frequencies of >100

77 Hz are facilitated by asynchronous, or myogenic, flight muscle present in endopterygote

78 (Coleoptera, Diptera, ) and exopterygote (Thysanoptera and Hemiptera) groups

79 (Dudley, 2000) where one nerve impulse can initiate several wingbeats through stretch-

80 activation caused by mechanical loading on the wing (Pringle, 1967). Thus, the highest

81 wingbeat frequencies are found in smaller members of these groups (Byrne et al., 1988).

82 Members from other orders possess large wings that they beat at lower frequencies relative

83 to other insects of comparable body mass e.g. and Neuroptera (Dudley, 2000)

84 and Orthoptera (Snelling et al., 2012, 2017). Larger wings can produce more force per beat

85 than smaller wings, and therefore fewer beats are needed per unit time. Moreover, larger

86 wings afford lower wing loadings for insects of the same body mass, so wingbeat frequency

87 may be reduced further. It is possible then that flight-associated morphometrics, such as wing

88 area, can be used to predict wingbeat frequency and characterize flight for different groups of

89 insect using the same stroke strategy (i.e. conventional wingbeat or clap-fling).

90

91 Flight morphology and wingbeat frequency are dependent on the aerodynamic needs of the

92 insect according to their ecological niche and oxygen consumption increases with wingbeat

93 frequency (Bartholomew and Casey, 1978). Species with similar wing loadings may have

94 different wingbeat frequencies based on the flight velocity required to fulfil their ecological role.

95 Substantial variation in wingbeat frequency and flight morphology as a product of ecological

96 needs also exists within orders, such as the differences between Sphingidae and Nymphalidae

97 (Lepidoptera), where sphingids have small wings, rapid beat frequencies and very fast flight,

98 whilst nymphalids have much larger wings and lower wingbeat frequencies, usually flying at

99 overall slower speeds (Dudley, 2000). Such variation could conceal relationships between

100 flight-associated morphometrics and wingbeat frequency across higher taxonomic levels,

101 decreasing the overall level of phylogenetic grouping of flight strategy. 102

103 Order-level taxonomic relationships to these flight-associated morphometrics have been

104 studied before (see Byrne et al., 1988; Dudley, 2000) but meta-analyses suffer from

105 differences in both ambient conditions and methods of measuring wingbeat frequency

106 between studies that may confuse relationships. For example, acoustic methods,

107 stroboscopes, and high-speed cameras were used across studies incorporated into Dudley

108 (2000) and Byrne et al.’s (1988) meta-analyses. Chadwick (1939) suggested stroboscopic

109 methods are difficult to use effectively to glean kinematic data in insects because of the slight

110 variations in wingbeat frequency and movements of the specimen during testing, making

111 visualisation of the wing at the frequency of the strobe light challenging and Unwin and

112 Ellington (1979) suggested picking up acoustic signals of smaller species difficult even with

113 highly sensitive microphones. Both stroboscopic (e.g. Chen et al., 2014) and acoustic (e.g.

114 Raman et al., 2007) methods have, however, been used successfully to measure wingbeat

115 frequency in insects since advancement in the quality of measurement instruments (i,e, optical

116 tachometers and microphones). Nevertheless, stroboscopic/optical and acoustic methods are

117 not absolute measures of wingbeat frequency. High-speed cameras, in contrast, allow the

118 recording of a temporally magnified visual depiction of the motion of insect wings. The

119 reliability of the methods used in studies incorporated into important meta-analyses varies

120 because of the problems faced when the technology was less well developed. Furthermore,

121 temperatures vary from 7-25oC between studies used in previous meta-analyses. Insect

122 wingbeat frequency has been shown to increase with higher temperatures (Unwin and Corbet,

123 1984; Oertli, 1989) and, therefore, meta-analyses of the relationships between measured

124 characteristics may be confounded. An experimental approach using high-speed cameras in

125 controlled conditions recording flight in species across several orders has not previously been

126 done. Using common UK species of insect, relationships between body mass, wing length,

127 wing area, wing loading and wingbeat frequency were investigated to determine if flight

128 strategies could be broadly characterized between different orders of insect. 129

130 MATERIALS AND METHODS

131 Study specimens

132 Adult insects were caught using either sweep net (EFE & GB Nets, Totnes, Devon, UK –

133 handle length = 0.3 m; net diameter = 0.5 m; net depth = 0.7 m), pooter (NHBS, Totnes, Devon,

134 UK – barrel diameter = 30 mm, length = 55 mm, suction tube diameter = 5mm), or hand

135 collected into small sampling pots (varying sizes) within a 20 km radius of Harper Adams

136 University, Shropshire, UK (latitude ~52.772°N, longitude ~2.411°W) over the course of June

137 and July, 2017. In total, 112 specimens across 102 species in 10 orders were used in the

138 analysis.

139

140 Filming area and conditions

141 Filming took place inside a Fitotron® Standard Growth Room unit (Weiss Technik, Ebbw Vale,

142 UK) set to a constant 20oC and 60% relative humidity. This temperature was selected to film

143 flight behaviour of insects in standardised conditions and is unlikely to represent an extreme

144 for tested species, which were all collected during summer days and therefore active within

145 ~±5oC of the ambient temperature used. Ambient lighting intensity was 280 μmol m-2 s-1 inside

146 the Fitotron® unit and no other external light source was used. A flight box made of 6

147 transparent Perspex® panels, measuring 30x30x30 cm once constructed, was used to contain

148 flights of the specimens whilst filming. Study specimens were introduced to the flight box either

149 via a 2.5 cm diameter aperture made in the centre of one of the panels by offering up an open

150 test tube containing a specimen, or, for larger specimens, the entire panel could be removed

151 and the specimen introduced.

152

153 Filming procedure 154 Each specimen was filmed 2-5 times using an FPS1000HD monochromatic high-speed

155 camera (The Slow Motion Camera Company, London, UK). Specimens were filmed each time

156 during free flight. For each flight recorded, the camera was handheld in order to track insects

157 in free flight. This helped increase total length of each video and thus more reliably count

158 wingbeats. Across videos, insects were filmed from various angles, but this did not affect video

159 analysis. Sufficient video footage was gathered in <10 minutes for each specimen.

160

161 Morphological measurements

162 Specimens were killed in a killing jar (a jar with a base of plaster of Paris to which ethyl acetate

163 was intermittently added when needed) after the last video was recorded and immediately

164 weighed using a precision balance (Cahn C-33 Microbalance, Cerritos, California, USA). The

165 functional wing (in insects with only one pair of functional wings e.g. Diptera and Coleoptera)

166 or wing couple on the right side (i.e. the fore- and hindwing on the right side of the insect

167 viewed dorsally) was removed by dissection under a stereo microscope and forewing length

168 (henceforth wing length) was measured using a pair of digital calipers (0.01 mm precision),

169 measured from the base of the forewing to the most distal tip. A photo was taken of the

170 dissected wing couple using a microscope camera making sure the wings were perpendicular

171 to the camera lens. Wing area was measured in ImageJ version 1.49 (Schindelin et al., 2012)

172 by using the photo and following the ImageJ process for measuring leaf area (Reinking, 2007)

173 as in previous studies on insect wings (e.g. Outomuro et al., 2013); the wing area value was

174 multiplied by 2 to quantify total wing area assuming symmetry. Wing loading was determined

175 by dividing body mass by total wing area.

176

177 Video analysis

178 Videos were first converted into a viewable format using ImageJ, where video frames-per-

179 second (FPS) was then altered to allow individual wingbeats to be clearly visible. A wingbeat 180 was judged to be both a full downstroke and full upstroke, terminating at pronation before the

181 next wingbeat (Fig. 1), and in all groups except for Odonata, fore- and hindwings beat at the

182 same time. For odonates, forewing and hindwing pairs were measured separately then the

183 mean was calculated; the difference between the wing pairs did not exceed 2 beats in any of

184 the odonate specimens. Sections of videos were carefully selected to represent free-flight,

185 omitting wingbeats immediately after take-off until a more regular rhythm was observed, which

186 was usually more rapid. The number of wingbeats nv during free-flight was counted for each

187 video. Equation 1 was used to determine the wingbeat frequency n (Hz) from each video

188 where tv is the length of the video in seconds, and fm is the multiplication factor (the factor that

189 describes by how much time is magnified in each video), which is calculated by dividing filming

190 FPS by video playback FPS. All species were filmed at 1000 FPS except for 6 species of

191 nematoceran Diptera, which were filmed at 2000 FPS.

192

193 Statistical Analysis

194 Statistical analysis was conducted using R version 3.4.1. “Single Candle” (R Core Team,

195 2017) with packages MASS (Venebles and Ripley, 2002), ggplot2 (Wickham, 2009), caret

196 (Kuhn, 2017), hydroGOF (Zambrano-Bigiarini, 2014), relaimpo (Grömping, 2006), and

197 gridExtra (Auguie, 2016) used. Both simple and multiple linear regression analyses were

198 conducted to determine the relationships between morphological variables and wingbeat

199 frequency. Data were log-transformed to reduce skew and allow analysis by linear regression.

200 To better measure the level of phylogenetic clustering of flight strategy, a principal component

201 analysis (PCA) was conducted.

202

203 RESULTS

204 Morphometric data 205 Table 1 compiles the range and mean statistics for morphological measurements and

206 wingbeat frequency in each sampled order. Across all 112 specimens, wingbeat frequency

207 covered a range between 12.468 to 557.351 Hz (̅ = 121.588, sd = 92.679, se = 8.767), body

208 mass a range of 0.0003 to 2.245 g (̅ = 0.097, sd = 0.256, se = 0.024), wing length a range of

209 0.172 to 5.214 cm (̅ = 1.184, sd = 0.919, se = 0.087), wing area a range of 0.022 to 23.362

210 cm2 (̅= 2.022, sd = 4.088, se = 0.386), and wing loading a range of 0.0028 to 0.245 g/cm2

211 (̅= 0.061, sd = 0.059, se = 0.006).

212

213 These values show that some orders were better sampled than others and in some cases this

214 is reflected in the ranges of different variables recorded. Average values, however, are

215 generally in agreement with expected values for UK insects. Synchronous fliers

216 (Ephemeroptera, Lepidoptera, Mecoptera, Neuroptera, Odonata, Trichoptera) were overall

217 less well sampled than asynchronous fliers (Coleoptera, Diptera, Hemiptera, Hymenoptera)

218 and should be similarly taken into account when considering ranges of variables.

219

220 Relationships between morphometrics and wingbeat frequency

221 Figure 2 shows the relevant linear relationships between the log10 transformed morphometric

222 data. Of these, wing area (cm2) was the best predictor of wingbeat frequency (R2 = 0.59, p =

223 <0.001). The strongest overall linear relationship between all morphometric measurements

224 was between wing length (cm) and wing area (R2 = 0.93, p = <0.001). Body mass explained

225 only 9% of the variation in wingbeat frequency across specimens (R2 = 0.09, p = <0.01) and

226 represented the poorest predictor of wingbeat frequency across the measured morphometrics.

227 Taxonomic distribution on the graphs (Fig. 2, especially A-D) sees a diffuse but identifiable

228 clustering of the orders most intensively sampled, suggesting that orders may broadly adhere

229 to a specific strategy and some new phylogenetic clustering between wingbeat frequency,

230 wing area, wing length, and wing loading have been revealed where previous meta-analyses 231 focussed solely on taxonomic grouping in relation to wingbeat frequency and body mass. For

232 example, looking at Figure 2D, Hymenoptera are quite closely clustered at the higher end of

233 the wing loading range and the upper-middle range of wingbeat frequency, denoting that most

234 hymenopterans sampled have small wings relative to their body mass, which they beat at

235 above average frequencies compared to other orders.

236

237 A multiple regression model using log10 values of wing area (β = -0.034, p = <0.001) and body

238 mass (β = 0.001, p = <0.001), with a fit of R2 = 0.84 was the best overall model predicting

239 wingbeat frequency in insects: wingbeat frequency = (wing area * -0.77) + (body mass * 0.37)

240 + 5.56.

241

242 A dominance analysis (Azen and Budescu, 2003; Grӧmping, 2006) was conducted to

243 determine the relative importance of the explanatory variables to the response variable in the

244 model and showed that body mass and wing area explained 17.3% and 67.2% of the change

245 in wingbeat frequency, respectively.

246

247 Phylogenetic clustering of flight strategy

248 Wingbeat frequency and morphometric variables for all specimens were reduced to a dataset

249 summarising the variance and covariance between each using a Principle Component

250 Analysis (PCA). Initial eigenvalues indicated the first two principle components explained

251 64.039% and 33.291% of the data, respectively (97.331% cumulatively). Dimension 1 is

252 mainly loaded towards wing area (30.417%), wing length (30.073%), body mass (22.882%),

253 and wing loading (16.388%), whereas Dimension 2 is mainly loaded towards wing loading

254 (58.325%), wingbeat frequency (24.979%), and body mass (15.821%). Having determined the

255 loadings, a PCA biplot was produced to view the relationship between variables and whether 256 insect orders were clustered on the graph. Figure 3 reveals that most insect specimens are in

257 close proximity to their associated centroid (the mean value of the x and y coordinates for

258 each order), shown by the ellipses, which represent one standard deviation along each axis

259 and is rotated toward the direction of maximum spread of the point cloud. This strongly

260 suggests that flight strategy is well conserved at the order level.

261

262 DISCUSSION

263 Phylogenetic clustering apparent in this study broadly agrees with results from previous meta-

264 analyses (Byrne et al., 1988; Dudley, 2000). Past research looking at differences in wingbeat

265 frequency and flight-associated morphometrics are, therefore, experimentally validated by the

266 present study through the use of high-speed filming. However, although all measured

267 characteristics significantly affected wingbeat frequency, body mass did not show as clear a

268 relationship to it as in previous meta-analysis (Dudley, 2000). This is likely because of the lack

269 of specimen variation in the present study, compared to the very high number of different

270 specimens across a much broader body mass range in the meta-analysis (Dudley, 2000).

271 Indeed, previous meta-analyses included species from a much wider geographical range,

272 incorporating studies from many different countries and ecosystems, including those from

273 tropical forests.

274

275 Wing length and wing area are both able to predict wingbeat frequency moderately accurately,

276 explaining 42% and 59% of its variation, respectively. Wing length may affect wingbeat

277 frequency as a product of increasing body mass, where larger insects have slightly longer

278 wings to offset the lower wingbeat frequency and maintain good advance ratios (Vogel, 2013),

279 though this is also connected to wing area (Fig. 2E). Area of the wing generally increases with

280 body mass to accommodate the greater level of lift generation required and longer wings tend

281 to have a greater area than shorter ones. Thus, an increased area means fewer beats are 282 necessary per unit time to generate the same amount of lift. This is supported by the positive

283 relationship between wing loading and wingbeat frequency, where heavily loaded wings are

284 generally beaten more rapidly to generate enough lift. Relatively heavily loaded wings must

285 keep a weight aloft with a reduced area and are associated with larger insects (Fig. 2F)

286 because wing area, proportional to the square of body length, cannot keep pace with body

287 mass, proportional to the cube of body length, as insect size increases (Bartholomew and

288 Heinrich, 1973; Byrne et al., 1988; Ennos, 1989; Dudley, 2000; Vogel, 2013). Despite this,

289 heavier insects tended to also have lower wingbeat frequencies (Fig. 2C). Whilst initially

290 paradoxical that heavier insects with greater wing loading beat their wings relatively less

291 frequently, this is because smaller insects must overcome the increasingly viscous forces of

292 air at small scales, greater relative drag, and the greater effect of the wind on their direction

293 by beating their wings comparatively faster (Dudley, 2000; Alexander, 2002; Vogel, 2013) and

294 because the oscillatory frequency of the thorax is inversely dependent on its size, which

295 directly influences wingbeat frequency in asynchronous fliers (Pringle, 1949, 1967; Dickinson

296 and Tu, 1997; Dudley, 2000).

297

298 The best overall model explaining the variation in wingbeat frequency incorporated body mass

299 and wing area, the relative importances of which were 17.3% and 67.2%, respectively. This

300 suggests that despite the weak linear relationship between body mass and wingbeat

301 frequency, together with wing area the variables can explain 84% of the variation in wingbeat

302 frequency. These findings support previous agreement (Jensen, 1956; Ellington, 1984b-c,

303 1999; Dudley, 1990, 2000; Alexander, 2002) that wingbeat frequency is in large part

304 dependent on wing area and body mass.

305

306 Palaeopterous insects using direct flight muscles and neopterous insects using synchronous

307 flight muscles show generally lower wingbeat frequencies than insects with asynchronous 308 flight muscles (Figure 2) and these two groups are further clustered in Figure 3 (Neuroptera,

309 Lepidoptera, Odonata – bottom right; asynchronous fliers – middle/top left). The weak

310 relationship between wingbeat frequency and body mass in the present study as well as past

311 meta-analyses may arise because of the differences in scaling between these groups. Insects

312 with indirect synchronous flight muscles conduct wingbeats by single nerve impulses to the

313 tergosternal (wing depressor) and dorsal-longitudinal (wing elevator) muscles. Thus, the

314 wingbeat frequency of insects with synchronous musculature is determined by the frequency

315 of nervous stimulation to the muscles. In contrast, insects that possess asynchronous muscles

316 have essentially random nervous stimulation relative to the wingbeat frequency (Dickinson

317 and Tu, 1997). Wingbeat frequency in asynchronous fliers is determined primarily by the

318 resonant features of the pterothoracic apparatus to maximise efficiency of energy expenditure

319 (Pringle, 1949; Dickinson and Tu, 1997), as well as behavioural changes during rapid

320 manoeuvring (Nachtigall and Wilson, 1967). Asynchronous muscles are stretch-activated

321 (Pringle, 1949, 1967) by their antagonistic pair within the pterothorax and are therefore

322 dependent on mechanical loading. The inertial load of the whole thorax-wing system must

323 increase with body mass and wingbeat frequency has been shown to vary inversely with wing

324 inertia (Sotavalta, 1952). For asynchronous fliers, scaling of the resonant flight apparatus is

325 therefore especially important, as the oscillatory frequency of the pterothorax is inversely

326 dependent on its size, which directly influences wingbeat frequency (Pringle, 1949, 1967;

327 Dickinson and Tu, 1997; Dudley, 2000). In synchronous fliers, wing amputation experiments

328 to lower wing inertia results in only a relatively small increase in wingbeat frequency in

329 Periplaneta cockroaches and Agrontia moths compared to asynchronous fliers (Roeder,

330 1951), suggesting wingbeat frequency in synchronous fliers is independent of mechanical

331 load. Thus, asynchronous fliers are more likely to show a stronger scaling relationship

332 between wingbeat frequency and body mass than other insects. No strong inferences relating

333 to scaling differences between synchronous and asynchronous fliers can be made in the

334 present study because Lepidoptera encompassed the only well sampled synchronous fliers. 335

336 Orders are shown to be clustered when wingbeat frequency is viewed as a function of one of

337 the other measured morphometrics (Fig. 2A-C), supporting the idea that flight strategy can be

338 generally characterized based on evolutionary history. This may be because of a combination

339 of several factors: 1) species inherit a flight apparatus that can only be changed to a certain

340 extent in a given time to fit a new role/niche e.g. Coleoptera inherit heavy elytra, one pair of

341 functional wings, asynchronous flight muscles, and low flight muscle mass ratio relative to

342 body mass (Marden, 1987; Dudley, 2000) making it unlikely for them to be able to fill the role

343 of an aerial predator but well adapted to infrequent spells of sustained flight; 2) species may

344 need to in the same way even though they have different ecological niches, which may

345 increase the level of intra-order clustering because the existing flight apparatus can be used

346 to fulfil the same aerodynamic needs despite interacting with different organisms e.g.

347 Syrphidae and Tabanidae need to fly in similar ways – visiting flowers vs. visiting vertebrate

348 hosts (female tabanids), ability to hover above resources, ability to change direction rapidly to

349 regularly escape predators or swatting etc.; and 3) a specific goal may be achieved in more

350 than one way e.g. Diptera: Asilidae and Odonata are both aerial predators with a high

351 proportion of relative flight muscle mass (Marden, 1987), but likely utilise completely different

352 flight strategies because of their very different inherited flight apparatuses. Combined, these

353 factors suggest that although an inherited flight apparatus is predisposed to certain flight

354 strategies and precludes others, it can be somewhat modified in some instances to fit new

355 ecological niches or maintained if aerodynamic needs do not change with differing ecological

356 interactions. Ultimately, this may improve levels of flight strategy conservation at the order

357 level.

358

359 Order-level flight strategies may have interesting energetic, ecological, and evolutionary

360 implications though intra-order exceptions exist where some groups fly in unconventional

361 ways. For example, flies are very light to medium weight with high wingbeat frequencies, 362 medium to low wing area and wing length, and medium to high wing loading (Fig. 2A-D). These

363 attributes afford flies the ability to fly quickly, perform complex aerobatic manoeuvres and to

364 hover, conferring obvious ecological advantages to certain groups. Mosquitos and

365 chironomids, however, possess wingbeat frequencies that are unusually high, and wing

366 loadings that are unusually low relative to other Diptera (Table S1, Supplementary Information)

367 that likely increases energetic costs of flight substantially, and may be used for acoustic

368 communication during swarming and mating (Neems et al., 1992; Takken et al., 2006;

369 Bomphrey et al., 2017). One potential explanation of this presumably highly energetically

370 expensive trait uncharacteristic of most other members of the order may be related to sexual

371 selection, where males and females “duet” by reaching a common harmonic tone based on

372 their usually different wingbeat frequencies (Cator et al., 2009; Robert, 2009; Bomphrey et al.,

373 2017).

374

375 The variation between different clades within orders suggests broad categorization is possible,

376 with infrequent exceptions. For most orders, however, relationships between wingbeat

377 frequency and flight-associated morphometrics show moderately well conserved patterns

378 across the graphs. These align with previous meta-analyses (Byrne et al., 1988; Dudley, 2000)

379 looking at wingbeat frequency in relation to body mass, with the same orders covering the

380 same areas on the graphs (see Fig. 3.3B in Dudley, 2000). The present study therefore

381 provides strong experimental evidence that flight strategy is broadly conserved at the order

382 level, as specimens are generally clustered phylogenetically, and this validates previous meta-

383 analyses investigating wingbeat frequency and flight-associated morphometrics, although

384 there is evidence that some flight strategies show similarity between certain groups. The PCA

385 analysis could though be improved by incorporating other variables, such as relative flight

386 muscle mass, which is shown to be important when considering the ecology of different orders

387 (Marden, 1987; Dudley, 2000).

388 389 Energetic and ecological costs and benefits of differing flight behaviours are still poorly known

390 in most insect groups, though some have received attention e.g. Hymenoptera: Apidae:

391 Euglossini (see Casey et al., 1985; Dudley, 1995; Dillon and Dudley, 2004), Lepidoptera:

392 Sphingidae and Saturniidae (Bartholomew and Casey, 1978), Orthoptera: Acrididae (Snelling

393 et al., 2012), and Hymenoptera: Apidae: Bombini (Ellington et al., 1990). Elucidation of the

394 ecological pressures leading to adaptation of specific flight strategies and the energetic costs

395 associated may help illuminate evolutionary trade-offs. These trade-offs are likely to explain

396 the phylogenetic clustering found across flight-associated morphometrics and wingbeat

397 frequency in the present study. Studies that combine quantitative evaluation of insect flight

398 energetics with additional qualitative comparisons between orders can go some way in

399 revealing why different groups utilise different flight strategies (e.g. between bees, moths, and

400 locusts in Snelling et al., 2012). Further work to reveal ecological pressures and energetic

401 costs of broad flight strategies in different orders is therefore required to infer why insect

402 groups fly the way they do.

403

404 ACKNOWLEDGEMENTS

405 The authors would like to thank Danielle Klassen, George Hicks, and Todd Jenkins for

406 technical assistance during flight filming and field work and Aidan Thomas, Joe Roberts, and

407 Todd Jenkins for providing some of the specimens. George Hicks and Todd Jenkins for their

408 assistance with species .

409

410 COMPETING INTERESTS

411 The authors declare no competing or financial interests.

412

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552 553 Figure 1. Images a-k show a complete wingbeat in the beetle Rutpela maculata

554 (Coleoptera: Cerambycidae); t is time in milliseconds from the start of the wingbeat. a. the

555 end of pronation; b-e. downstroke translation; e-g. supination; h-j. upstroke translation; j-k.

556 pronation.

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573 574 Figure 2. Relationships between log10 transformed morphometric variables. a, wingbeat

575 frequency (Hz) as a function of wing length (cm): wingbeat frequency = -0.764 * wing length +

576 4.479, R2 = 0.42, p = <0.001; b, wingbeat frequency as a function of wing area (cm2): wingbeat

577 frequency = -0.413 * wing area + 4.345, R2 = 0.59, p = <0.001; c, wingbeat frequency as a

578 function of body mass (g): wingbeat frequency = -0.129 * body mass + 4.04, R2 = 0.09, p =

579 <0.01; d, wingbeat frequency as a function of wing loading (g/cm2): wingbeat frequency =

580 0.385 * wing loading + 5.799, R2 = 0.29, p = <0.001; e, wing area as a function of wing length:

581 wing area = 2.105 * wing length – 0.307, R2 = 0.93, p = <0.001; f, wing loading as a function

582 of body mass: wing loading = 0.356 * body mass – 1.977, R2 = 0.34, p = <0.001; g, wing area

583 as a function of body mass: wing area = 0.644 * body mass + 1.977, R2 = 0.63, p = <0.001.

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595 596 Figure 3. Principle component data for Dimensions 1 and 2, categorised into different

597 insect orders by symbol shape and colour. Small translucent symbols represent

598 specimens and large opaque symbols represent the centroids for each order. The ellipses

599 around each centroid represent one standard deviation along each axis of the associated

600 order and are rotated in the direction of maximum spread. Trichoptera, Ephemeroptera, and

601 Mecoptera lack ellipses because of an insufficient sample size. The Dimension scores show

602 a moderate-high level of clustering of orders in relation to measured variables, as specimens

603 are generally in close proximity to their associated centroid. The black point in the top-right

604 quarter of the graph is the mean direction of the arrows and suggests the variables are on

605 average positively correlated with dimensions 1 and 2.

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618 619 Table 1. Range and mean of wingbeat frequency and associated morphological

620 measurements in each sampled order. Number of species are denoted in parentheses beside

621 sample size in the right-most column.

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639 640 Supplementary Information

641 Table S1. Wingbeat frequency and morphological measurements of all specimens. Lists

642 specimens by body mass in ascending order. Cells with a “-“ denote the specimen failed to

643 be identified to the associated taxonomic rank.

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661 662 Figure 1.

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675 676 Figure 2.

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696 697 Table 1.

Wingbeat frequency (mean Number of ` Bodymass (g) Wing length (cm) Wing area (cm2) Wing loading (g/cm2) Hz) specimens range mean range mean range mean range mean range mean Coleoptera 79 - 123.396 97.512 0.0061 - 0.117 0.0539 0.521 - 1.188 0.898 0.19 - 0.982 0.545 0.0321 - 0.141 0.085 10(10)

Diptera 59.567 - 557.351 208.244 0.0005 - 0.162 0.0268 0.172 - 1.739 0.729 0.022 - 1.17 0.327 0.0119 - 0.168 0.0554 28(28)

Ephemeroptera n/a 75.0454 n/a 0.0027 n/a 0.634 n/a 0.306 n/a 0.00882 1(1)

Hemiptera 90.222 - 152.247 116.39 0.0011 - 0.14 0.0226 0.345 - 1.185 0.624 0.112 - 1.186 0.445 0.009 - 0.118 0.034 11(11)

Hymenoptera 87.129 - 230.987 163.89 0.0024 - 0.223 0.103 0.356 - 1.48 1.006 0.038 - 1.234 0.64 0.022 - 0.245 0.136 24(15)

Lepidoptera 12.468 - 64.566 39.606 0.0044 - 2.24 0.203 0.646 - 5.214 1.792 0.318 - 23.362 5.031 0.004 - 0.096 0.025 22(22)

Mecoptera n/a 48.885 n/a 0.0398 n/a 1.387 n/a 1.492 n/a 0.027 1(1)

Neuroptera 25-923 - 94.413 52.801 0.0003 - 0.0065 0.0035 0.352 - 1.393 0.757 0.106 - 1.972 0.701 0.003 - 0.007 0.005 6(6)

Odonata 17.847 - 40.665 32.331 0.0278 - 1.23 0.27 1.795 - 5.158 3.002 1.964 - 22.784 8.768 0.0112 - 0.054 0.022 8(6)

Trichoptera n/a 27.515 n/a 0.159 n/a 2.267 n/a 4.738 n/a 0.0336 1(1) 698

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710 711 Supplementary Table.

Wingbeat frequency Bodymass Wing length Wing area Wing loading Species Genus Family Order (mean Hz) (g) (cm) (cm2) (g/cm2)

- Micromus Hemerobiidae Neuroptera 94.413 0.0003 0.352 0.106 0.003

- Syrphus. Syrphidae Diptera 190.860 0.0005 0.172 0.022 0.023

- - Psychodidae Diptera 144.611 0.0006 0.267 0.048 0.013

- - - Diptera 204.355 0.0009 0.252 0.050 0.018

- - Chironomidae Diptera 557.351 0.0011 0.406 0.064 0.017

- - Miridae Hemiptera 127.872 0.0011 0.345 0.122 0.009

Thaumatomyia notata Thaumatomyia Chloropidae Diptera 269.741 0.0014 0.234 0.038 0.037

- - - Hemiptera 152.247 0.0014 0.358 0.114 0.012

Uroleucon cirsii Uroleucon Aphididae Hemiptera 99.603 0.0015 0.353 0.112 0.013

- - Chironomidae Diptera 544.494 0.0018 0.423 0.070 0.026

- - Tipulidae Diptera 94.606 0.0020 0.687 0.168 0.012

Hemerobius humulinus Hemerobius Hemerobiidae Neuroptera 46.583 0.0021 0.586 0.336 0.006

- Torymus Torymidae Hymenoptera 160.011 0.0024 0.360 0.098 0.024

Wesmaelius subnebulosis Wesmaelius Hemerobiidae Neuroptera 45.304 0.0026 0.640 0.416 0.006

Centroptilum luteolum Centroptilum Baetidae Ephemeroptera 75.045 0.0027 0.634 0.306 0.009

- - Braconidae Hymenoptera 136.261 0.0029 0.438 0.134 0.022

- - Chloropidae Diptera 180.050 0.0030 0.342 0.070 0.043

- - Braconidae Hymenoptera 164.443 0.0030 0.356 0.038 0.079

- Wesmaelius Hemerobiidae Neuroptera 54.583 0.0033 0.666 0.444 0.007

- - - Diptera 195.996 0.0039 0.354 0.098 0.040 - - Syrphidae Diptera 198.890 0.0044 0.403 0.124 0.035

Pseudargyrotoza conwagana Pseudargyrotoza Lepidoptera 64.246 0.0044 0.653 0.318 0.014

- - Miridae Hemiptera 120.832 0.0048 0.530 0.250 0.019

Culex pipiens Culex Culicidae Diptera 334.037 0.0049 0.578 0.158 0.031

- - Tortricidae Lepidoptera 52.214 0.0055 0.785 0.612 0.009

- - Crambidae Lepidoptera 57.948 0.0059 0.799 0.562 0.010

Micromus angulatus Micromus Hemerobiidae Neuroptera 50.000 0.0059 0.904 0.932 0.006

Oulema melanopus Oulema Chrysomelidae Coleoptera 123.398 0.0061 0.521 0.190 0.032

Chrysoperla carnea Chrysoperla Chrysopidae Neuroptera 25.923 0.0065 1.393 1.972 0.003

Aedes cantans Aedes Culicidae Diptera 286.949 0.0066 0.627 0.182 0.036

Culiseta annulata Culiseta Culicidae Diptera 344.160 0.0070 0.572 0.150 0.047

Macrolophus sp. Macrolophus Miridae Hemiptera 139.717 0.0076 0.515 0.244 0.031

Lobesia abscisana Lobesia Tortricidae Lepidoptera 64.566 0.0076 0.646 0.526 0.014

Culiseta annulata Culiseta Culicidae Diptera 331.157 0.0077 0.602 0.174 0.044

- - - Hemiptera 116.865 0.0104 0.619 0.490 0.021

Propylea 14-punctata Propylea Coccinellidae Coleoptera 102.427 0.0105 0.589 0.210 0.050

Pasiphila rectangulata Pasiphila Geometridae Lepidoptera 41.358 0.0107 0.938 1.132 0.009

Pterophorus pentadactyla Lepidoptera 32.333 0.0114 1.192 1.120 0.010

Nephrotoma flavescens Tipulidae Diptera 79.470 0.0118 1.015 0.402 0.029

- - Miridae Hemiptera 108.171 0.0119 0.710 0.402 0.030

Pandemis cerasana Pandemis Tortricidae Lepidoptera 54.184 0.0124 0.835 0.890 0.014

Athalia scuttelariae Athalia Tenthredinidae Hymenoptera 87.129 0.0132 0.826 0.352 0.038

Xanthorhoe montanata Xanthorhoe Geometridae Lepidoptera 29.243 0.0133 1.561 2.980 0.004

Lygus rugulipennis Lygus Miridae Hemiptera 115.183 0.0140 0.574 0.298 0.047 Nephrotoma quadrifaria Nephrotoma Tipulidae Diptera 67.360 0.0181 1.084 0.464 0.039

Rhagonycha fulva Rhagonycha Catharidae Coleoptera 79.712 0.0183 0.653 0.380 0.048

Chloromyia formosa Chloromyia Stratiomyidae Diptera 156.043 0.0183 0.736 0.312 0.059

Haematopota pluvialis Haematopota Tabanidae Diptera 151.568 0.0183 0.779 0.302 0.061

- - - Hemiptera 112.917 0.0184 0.682 0.518 0.036

- - Vespidae: Eumeninae Hymenoptera 135.597 0.0184 0.703 0.292 0.063

- - Empididae Diptera 151.321 0.0193 0.759 0.288 0.067

Oedemera nobilis Coleoptera 112.656 0.0210 0.698 0.232 0.091

Scathophaga stercoraria Scathophaga Scathophagidae Diptera 104.015 0.0224 0.854 0.366 0.061

- - Ichneumonidae Hymenoptera 110.116 0.0233 0.942 0.546 0.043

Manulea lurideola Lepidoptera 33.095 0.0258 1.470 2.542 0.010

Syrphus ribesii Syrphus Syrphidae Diptera 177.908 0.0273 0.994 0.512 0.053

Coenagrion puella Coenagrion Coenagrionidae Odonata 37.495 0.0277 1.795 1.964 0.014

Harmonia axyridis Harmonia Coccinellidae Coleoptera 79.000 0.0283 0.993 0.644 0.044

Anania hortulata Anania Crambidae Lepidoptera 40.996 0.0293 1.448 2.410 0.012

Episyrphus balteatus Episyrphus Syrphidae Diptera 166.057 0.0294 1.025 0.488 0.060

Idaea aversata Idaea Geometridae Lepidoptera 32.088 0.0303 1.471 2.420 0.013

Coenagrion puella Coenagrion Coenagrionidae Odonata 36.691 0.0316 1.984 2.072 0.015

- Aphodius Scarabaeidae Coleoptera 93.054 0.0327 0.987 0.560 0.058

Aphantopus hyperantus Aphantopus Nymphalidae Lepidoptera 16.014 0.0373 2.168 7.262 0.005

Coenagrion puella Coenagrion Coenagrionidae Odonata 36.839 0.0374 1.902 2.190 0.017

- Andrena Apidae Hymenoptera 213.815 0.0376 0.703 0.352 0.107

- - - Hemiptera 90.222 0.0383 0.989 1.164 0.033

- - Syrphidae Diptera 208.540 0.0385 0.872 0.340 0.113 Panorpa communis Panorpa Panorpidae Mecoptera 48.885 0.0398 1.387 1.492 0.027

- Andrena Apidae Hymenoptera 172.581 0.0453 0.690 0.346 0.131

- Sarcophaga Sarcophagidae Diptera 149.643 0.0540 0.995 0.526 0.103

Calliphora vomitoria Calliphora Calliforidae Diptera 214.835 0.0549 0.874 0.460 0.119

Hypena proboscidalis Hypena Noctuidae Lepidoptera 30.587 0.0565 1.137 4.496 0.013

- Tipula Tipulidae Diptera 59.567 0.0676 1.739 1.170 0.058

Pieris brassicae Pieris Pieridae Lepidoptera 12.468 0.0691 2.593 10.992 0.006

Vespula germanica Vespula Vespidae Hymenoptera 145.156 0.0769 1.126 0.628 0.122

Ectemnius cavifrons Ectemnius Crabronidae Hymenoptera 210.688 0.0800 1.037 0.542 0.148

- Zygaena Zygaenidae Lepidoptera 60.595 0.0804 1.669 2.640 0.030

Vespula germanica Vespula Vespidae Hymenoptera 152.006 0.0818 1.061 0.610 0.134

Vespula germanica Vespula Vespidae Hymenoptera 146.908 0.0833 0.530 0.536 0.155

Vespula vulgaris Vespula Vespidae Hymenoptera 173.277 0.0874 1.081 0.598 0.146

Apis mellifera Apis Apidae Hymenoptera 230.987 0.0886 0.995 0.588 0.151

- Aphodius Scarabaeidae Coleoptera 103.159 0.0929 1.018 0.658 0.141

Rutpela maculata Rutpela Cerambicidae Coleoptera 86.840 0.1026 1.188 0.768 0.134

Geometra papilionaria Geometra Geometridae Lepidoptera 22.023 0.1071 2.632 10.194 0.011

Chrysoteuchia culmella Chrysoteuchia Crambidae Lepidoptera 40.626 0.1090 1.041 1.330 0.082

Calopteryx splendens Calopteryx Calopterygidae Odonata 19.318 0.1092 3.054 9.760 0.011

- Aphodius Scarabaeidae Coleoptera 101.111 0.1095 1.161 0.822 0.133

Polygonia c-album Polygonia Nymphalidae Lepidoptera 27.501 0.1145 2.298 7.696 0.015

Bombus pascuorum Bombus Apidae Hymenoptera 198.274 0.1166 1.074 0.614 0.190

Leptura quadrifasciata Leptura Cerambicidae Coleoptera 93.768 0.1173 1.173 0.982 0.119

Orthosia gothica Orthosia Noctuidae Lepidoptera 47.053 0.1253 1.753 3.260 0.038 Pentatoma rufipes Pentatoma Pentatomidae Hemiptera 96.667 0.1397 1.185 1.186 0.118

Calopteryx virgo Calopteryx Calopterygidae Odonata 17.847 0.1457 3.287 10.466 0.014

Bombus terrestris ♀ Bombus Apidae Hymenoptera 183.029 0.1504 1.171 0.818 0.184

Bombus lapidarius Bombus Apidae Hymenoptera 199.547 0.1536 1.114 0.626 0.245

Phryganea grandis Phryganea Phryganeidae Trichoptera 27.515 0.1590 2.267 4.738 0.034

Sympetrum striolatum Sympetrum Libellulidae Odonata 40.665 0.1595 2.894 6.944 0.023

Volucella pellucens Syrphidae Diptera 134.179 0.1613 1.418 1.152 0.140

Volucella bombylans Volucella Syrphidae Diptera 133.078 0.1624 1.337 0.966 0.168

Bombus terrestris ♀ Bombus Apidae Hymenoptera 186.521 0.1641 1.229 0.864 0.190

Bombus terrestris ♀ Bombus Apidae Hymenoptera 152.625 0.1854 1.436 1.048 0.177

Bombus terrestris ♀ Bombus Apidae Hymenoptera 153.182 0.1972 1.422 1.128 0.175

Bombus terrestris ♀ Bombus Apidae Hymenoptera 144.712 0.2081 1.454 1.134 0.184

Bombus terrestris ♀ Bombus Apidae Hymenoptera 161.815 0.2125 1.425 1.086 0.196

Bombus terrestris ♂ Bombus Apidae Hymenoptera 165.085 0.2154 1.480 1.152 0.187

Bombus terrestris ♂ Bombus Apidae Hymenoptera 149.597 0.2227 1.480 1.234 0.180

Orthetrum cancellatum Orthetrum Libellulidae Odonata 38.577 0.4176 3.944 13.960 0.030

Deilephila elpenor Deilephila Sphingidae Lepidoptera 53.715 0.5281 3.026 6.792 0.078

Laothoe populi Laothoe Sphingidae Lepidoptera 29.330 0.8449 4.085 17.152 0.049

Aeshna grandis Aeshna Aeshnidae Odonata 31.214 1.2296 5.158 22.784 0.054

Acherontia atropos Acherontia Sphingidae Lepidoptera 29.160 2.2403 5.214 23.362 0.096

712

713

714 715 Equation 1.

716 Equation 1

717